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15/9/2018 About the Journal | ASRO JOURNAL - STTAL
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ASRO JOURNAL-STTAL (ISSN : 2460-7037)
ASRO JOURNAL-STTAL is annual international referred journal with the objectives to explore,
develop, and elucidate the knowledge of Operations Management, Operations Research,
Logistic Management, Policy and Strategic to keep practitioners and researchers informed on
current issues and best practices, as well as serving as a platform for the exchange of ideas,
knowledge, and expertise among technology researchers and practitioners.
ASRO JOURNAL-STTAL provides an opportunity to share detailed insights from different
understandings and practices associated with technology. It provides an international forum for
cross-disciplinary exchange of insights and ideas regarding value and practices for
dissemination. ASRO Journal will publish your work to international society of practitioners and
researchers with interest in technology and development from a wide variety of sectors.
AIM AND SCOPE
The objective of the ASRO JOURNAL-STTAL is to provide an international forum for discussions of
advancements in Operations Management, Operations Research, Logistic Management, Policy
and Strategic Scope.
The Conference publishes high-quality research articles in all of these areas, and particularly
encourages articles dealing with international issues, practical experiences, pedagogical aspects
including case research, developments in the emerging nations, service management, and other
nontraditional and innovative areas. Purely theoretical articles should make significant
contribution to the existing knowledge.
It is necessary that all parties involved in publishing process: the authors, the editor-in-chief,
editorial board, the peer reviewers, and the publisher agree to abide by the highest standards of
ethical behavior of research publication.
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15/9/2018 About the Journal | ASRO JOURNAL - STTAL
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ASRO JOURNAL - STTAL is supported by INDONESIAN NAVAL INSTITUTE OF TECHNOLOGY -
STTAL
Journal Manager : Admiral Ir. Avando Bastari, M.Phil
: Captain Navy Dr. I Nengah Putra A, ST. M.Si (Han)
Managing Editor : Dr. Ahmadi, S.Si, MT
Editor In Chief : Dr. Okol Sri Suharyo, ST, MT
INDONESIAN NAVAL INSTITUTE OF TECHNOLOGY - STTAL
Bumimoro Surabaya Indonesia
Telp. +623199000583, Email : [email protected] ; [email protected] ;
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Journal Manager : Admiral Ir. Avando Bastari, M.Phil
: Captain Navy Dr. I Nengah Putra A, ST. M.Si (Han)
Managing Editor : Captain Navy Dr. Ahmadi, S.Si. MT.
Editor in Chief : Lieutenant Commander Navy Dr. Okol Sri Suharyo, ST. MT.
Editorial Team : Prof. Tutut Herawan (University of Malaya, Malaysia) ; Prof. Ir. Suparno, MSIE,
PhD (ITS) ; Prof. Ir. Budi Santoso, M.Sc, PhD (ITS)
INDONESIAN NAVAL INSTITUTE OF TECHNOLOGY - STTAL
Surabaya Indonesia
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15/9/2018 Vol 9 No 1 (2018): International Journal of ASRO | ASRO JOURNAL - STTAL
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HOME ARCHIVES Vol 9 No 1 (2018): International Journal of ASRO
Available Now
PUBLISHED: 2018-09-10
THE OPTIMIZATION OF MULTIPURPOSE BUILDING DEVELOPMENT ON PROJECTSCHEDULING USING PRECEDENCE DIAGRAM METHOD (PDM)
APPLICATION OF DECISION SUPPORT SYSTEM FOR DETERMINING AMPHIBIOUS LANDINGBEACH IN WEST PAPUA INDONESIA
/ /
ARTICLES
Arica Dwi Susanto, Ahmadi Ahmadi, Okol S Suharyo1-7
Budi Setiarso, Udisubakti Ciptomulyono, Bambang Suharjo, I Nengah Putra, A K Susilo8-22
15/9/2018 Vol 9 No 1 (2018): International Journal of ASRO | ASRO JOURNAL - STTAL
http://asrojournal-sttal.ac.id/index.php/ASRO/issue/view/12 2/5
RISK MANAGEMENT ON THE WARSHIP TASK OPERATIONS
PATROL SHIPS ASSIGNMENT MODEL IN EASTERN AREA OF INDONESIA WATER USINGSET COVERING
ANALYSIS OF MENTAL WORKLOAD WITH INTEGRATING NASA TLX AND FUZZY METHOD
RAILWAY AND PORTS INTEGRATION SYSTEM AS SOLUTION OF ISLAND TRANSPORTATIONPROBLEM (CASE STUDY SUMATRA ISLAND)
SELECTION OF ALTERNATIVE ENERGY SOURCES INDONESIAN WARSHIP PATROL CRAFT36 CLASS USING LIFE CYCLE COST (LCC) AND TOPSIS (TECHNIQUE FOR ORDERPREFERENCE BY SIMILARITY TO IDEAL SOLUTION)
SONIC LAYER DEPTH VARIATION ANALYSIS UTILIZING BIDE (BANDA ITF DYNAMICEXPERIMENT) ARGO FLOAT IN SITU OBSERVATION FOR UNDERSEA WARFARE TACTICALENVIRONMENT SUPPORT
Diksono Diksono, I Made Jiwa, Suparno Suparno23-29
Oyu Mulia S, Ahmadi Ahmadi, Okol S Suharyo30-36
Riono Riono, Suparno Suparno, Adi Bandono37-45
Satrio Teguh Amandiri, Ahmadi Ahmadi, Budi Santoso W47-51
Haryanto Wibowo, Ahmadi Ahmadi, Okol S Suharyo52-61
D Armansyah, N B Sukoco, W S Pranowo62-73
15/9/2018 Vol 9 No 1 (2018): International Journal of ASRO | ASRO JOURNAL - STTAL
http://asrojournal-sttal.ac.id/index.php/ASRO/issue/view/12 3/5
ROLE OF THE INDONESIAN NAVY TASK UNIT TO SUPPORTING TECHNOLOGY TRANSFEROF SUBMARINE BY DSME SOUTH KOREA-PT PAL INDONESIA
SUPPLIER SELECTION AND ORDER QUANTITY ALLOCATION OF RAW MATERIAL USINGINTEGER LINEAR PROGRAMMING
QUALITY ANALYSIS OF EDUCATIONAL SERVICES ON STUDENT RESPONSE THROUGH QFDAPPROACH TO IMPROVE PERFORMANCE OF UNIVERSITY OF 17 AGUSTUS 1945 (UNTAG)BANYUWANGI
A GENETIC ALGORITHM APPROACH FOR SOLVING INBOARD OUTER FIXED LEADINGEDGE-DRIVE RIB 1 PRODUCTION SCHEDULING AT PT DIRGANTARA INDONESIA
THE DETERMINATION OF SUBMARINE TRAINING WITH FUZZY MULTI CRITERIA DECISIONMAKING APPROACH (FMCDM )
DESIGN OF SURGE AND ROLL MOTION CONTROL SYSTEM OF ITSUNUSA AUV USING PIDCONTROLLER
SCHEDULING OPTIMIZATION OF KRI ASSIGNMENTS WITH BINARY INTEGERPROGRAMMING TO SECURE THE KOARMATIM SEA AREA
Nurcahya Dwi Asmoro, Udisubakti Ciptomulyono, I Nengah Putra, Ahmadi Ahmadi, Okol S Suharyo74-85
Ika Deefi Anna, Putri Rani Fhiliantie98-105
Endang Suprihatin106-119
Ilhamsah Ilhamsah, Cahyadi Cahyadi, Yulianto Yulianto120-128
Wisono Setiawan, Okol S Suharyo129-138
Teguh Herlambang, Subchan Subchan139-142
M Agus Arif H, Budi Santoso W, Ahmadi Ahmadi, Okol S Suharyo
61
SONIC LAYER DEPTH VARIATION ANALYSIS UTILIZING BIDE (BANDA ITF DYNAMIC EXPERIMENT) ARGO FLOAT IN SITU OBSERVATION FOR UNDERSEA WARFARE TACTICAL ENVIRONMENT SUPPORT
D Armansyah1, N B Sukoco1, W S Pranowo1,2
1 Department of Hydrography Engineering, Indonesian Naval Postgraduate School (STTAL),
Jl. Pantai Kuta V No. 01, Ancol Timur, Jakarta, 14430, Indonesia 2 Marine Research Centre, Indonesian Ministry of Marine Affairs and Fisheries,
Jl. Pasir Putih II, Ancol Timur, Jakarta, 14430, Indonesia
ABSTRACT
Sonic Layer Depth is the vertical distance from surface to the depth where the speed of sound reach it’s local
maximum. Global Navy as well as Indonesan Navy operates in this kind of layers on a daily basis, wether it be
in an anti submarine warfare operation through its ships and aircrafts or in an anti-surface warfare operation
waged by its submarines. For the navy, the importance of knowing the exact value of the SLD is because it
determines the minimum cut off frequency and sound wave propagation above which sound tends to be trapped
and below which a shadow zone exist. This has a direct impact on where the navy operates its sensors and
places its platforms in the water column. The best way to estimate SLD is by performing own measurement on
the ocean using instrument such as expendable bathytermographs, which can be relatively expensive and time
consuming for an operational navy ships, furthermore such measurement may be impractical in the case of
mission planning or emergencies. Dedicated oceanography survey for the purpose of science and defense is
relatively scarce due to prioritization concerning to the budget available resulting lack of time series in situ CTD
observation which cover full annual cycle variation in Indonesia waters. Banda Indonesia Through Flow
Dynamic Experiment (BIDE) is multi institution and bilateral oceanography experiment led by Indonesian
scientist which utilizes argo float to perform in situ CTD measurement in Banda Sea to get an adequate time
series data that cover full annual cycle variation. The dataset is publicly available so defense community as well
may utilize the data for defense interest such as sonic layer depth variation analysis in Banda Sea.
Keyword : sonic layer depth, BIDE, argo float, undersea warfare, tactical environment.
1. INTRODUCTION.
Sonic Layer Depth (SLD) is the vertical
distance from the sea surface to the depth where
the speed of sound underwater reachs it’s
maximum value. The maximum value of sound
speed occurs at certain depth due to under a
condition when temperature and salinity in a layer is
constant, the value of sound speed will increases
with pressure/depth until generally temperature,
and therefore sound speed decreases. SLD is an
important phenomenon because it characterizes
the capability or potential of the upper ocean or
surface layer to trap acoustic energy in a surface
duct. Nevertheless, SLD is not commonly known
and researched parameter if compared to another
parameter called Mixed Layer Depth (MLD). MLD is
the thickness of a ocean surface layer that has
almost constant value of temperature, salinity and
density, therefore by definition is different with SLD
because MLD characterizes how the mixing of
upper ocean layer. SLD and MLD are frequently
coincide due to the condition that sound speed
International Journal of ASRO Volume 9, Number 1, pp. 62-73
January-June 2018
62
increases with depth down to the MLD, where
generally the temperature declines resulting in a
local maximum sound speed. Previous reasearchs
shown that SLD and MLD are not always the same
because sound speed is substantially more
dependent to temperature than to salinity compared
to density. However scientists and practicioners
oceanographers often used MLD as a proxy or
substitute of SLD for scientific or operational
activities (Helber, et al., 2008).
Estimating the SLD variability is important for
understanding the acoustic properties of the upper
ocean that influence operational activities such as
navy operations related to hiding and detecting
marine underwater vessels (Urick, 1983). Navy
around the world has a vested interest in knowing
exactly where the MLD and SLD especially are
located. Global Navy as well as Indonesan Navy
operates in both layers on a daily basis, wether it
be in an anti submarine warfare operation through
its ships and aircrafts or in an anti-surface warfare
operation waged by its submarines (Rizanny,
2017). For the navy, the importance of knowing the
exact value of the SLD is because it determines the
minimum cut off frequency and sound wave
propagation above which sound tends to be
trapped and below which a shadow zone exist. This
has a direct impact on where the navy operates its
sensors and places its platforms in the water
column (Villareal, 2014).
One of the classical problem facing
oceanographers is the scarcity of in situ
observations to be used in estimating the
temperature and salinity structure of the ocean.
Traditionally, they rely on climatologies or collective
average derived from all available historical data
(Teague, et al., 1990). The other alternative is
performing own measurement on the ocean using
instrument such as expendable bathytermographs,
which can be relatively expensive and time
consuming for an operational navy ships,
furthermore such measurement may be impractical
in the case of mission planning or emergencies
(Fox, et al., 2001).
Fig. 1. An example of SLD
(Guest, 2014)
Scarcity of in situ physical oceanography
measurement is also a big problem faced by
oceanography community in Indonesia especially in
the Indonesian Navy. Indonesian Navy Hydrography
and Oceanography Centre (Pushidrosal) is the
institue responsible for the planning and execution of
hydrography and oceanography survey for the public
civil and military interests. However, historically at
present and possibly in the near future,
oceanography survey is remain to be only small part
of Pushidrosal operations since there is bigger
national maritime strategic development program
that must be supported because Pushidrosal is one
of the stakeholders who is responsible for providing
updated nautical chart for the Indonesia maritime
transportation. Nevertheless, Pushidrosal keeps
maintaining relations with other national
oceanography institution not only for the interest of
Indonesian Navy but also for the advancement of
national oceanography society and its further
contribution for national maritime sector. One of the
most recent ocenaography society national
collaboration is Banda Indonesian-Throughflow
Dynamic Experiment 2017-2018 which is led by
scientist from Institut Pertanian Bogor (IPB) and
supported by Ristekdikti, LEGOS (France) and
IFREMER (France). This experiment is not only
introducing Argo float as an advance data collection
63
technology but also with this experiment time series
measurement data in Banda Sea will be established
for full annual cycle which is a huge progress for
Indonesia oceanography (Atmadipoera, et al., 2017).
The availability of full annual cycle argo float CTD in
situ measurement is a great opportunity for
Pushidrosal to analyze it for providing tactical
environment support to Indonesian Navy undersea
warfare operations especially in Banda Sea.
1. DATA SOURCE AND METHODOLOGY
1.1. Argo floats superiority as
oceanography data acquisition
system
Previous studies show that Banda Sea
plays a significant roles such as region of
high primary productivity and pelagic fish
resources, seasonal Ekman upwelling
around outer Banda Arcs and active ocean-
atmosphere interactions which forced by
monsoon winds, ENSO etc. Eventhough
Banda Sea plays such an important role,
adequate oceanography measurements is
never been performed to collect time series
data that will cover full annual cycle
variations. Based on this condition a
research team of IPB under coordination of
Dr. Agus Atmadipoera with the colaboration
and support from Ristekdikti (Indonesia),
LEGOS (France) and IFREMER (France)
initiated Banda Indonesian-Through Flow
Dynamic Experiment (BIDE). The experiment
is utilizing CTD argo float measurement that
was deployed on July 29th 2017 onboard
inter-islands passenger ship namely MV
Kapal Perintis. The argo float was configured
to perform daily CTD profiling from surface to
1000 m depth, scheduled for surfacing every
23:00 local time meanwhile it is drifting
depends on the surrounding ocean current
(Atmadipoera, et al., 2017).
Fig. 2 Argo float BIDE picture
(jcommops, 2017)
Fig. 3 Argo float BIDE trajectory as of July 2018
(Atmadipoera, 2017)
According to the research team report
there are three configuration changes since
the first deployment of the argo float.
Configuration setting 01 was used from July
to September 2017 with default configuration
daily profiling and surfacing, configuration
setting 02 was used from September to
October 2017 with the changing of near-
surface resolution and 2-day profiling and
surfacing and lastly configuration setting 03
was used from October 2017 up to now with
another changing of near-surface resolution
sampling while maintaing the 2-day profiling
and surfacing for prolonging the battery
lifetime.
Fig. 4 Argo float BIDE configuration setting
(Atmadipoera, et al., 2017)
64
In order to download the argo float
measurement data for full annual cycle from
July 2017 up to July 2018 author accessed
the jcommops web site as directed by the
research team. There are several options
collection data prepared by IFREMER, NRL
and NOAA either in netcdf format or txt
(jcommops, 2017). For the purpose of this
research author chose to download data
prepared by NOAA in netcdf format (NOAA,
2018).
Before BIDE actually, there was joint
researh on global climate change impact
utilizing 2 unit of argo floats between Agency
for Marine and Fisheries Research under
Indonesian Ministry of Marine Affairs and
Fisheries and CSIRO Australia. This program
was recorded as the first involvement of
Indonesian scientist to a project utilizing argo
floats as the most preferable data acquisition
utility system by the International
oceanography society which are relatively
easy and less expensive compared to the
other system (Pranowo, et al., 2003). The
next two following years the acquired data
measurements were already available and
analyzed by the team for describing
upwelling event 2003 along South Java Sea
and Lesser Sunda Islands(Pranowo, et al.,
2005). Another best practices of argo floats
data utilization by Indonesian scientist was
published just recently which was studying
temperature and salinity stratification in the
Eastern Indian Ocean using argo float
dataset from 1999 up to 2016 with about
hundreds profiles per year which is difficult to
be matched by traditional data acquisition
system such as CTD measurement onboard
a research vessel (Purba, et al., 2018).
1.2. Description of Data Source
The data source was retrieved from
jcommops website with filename
nodc_6901746_prof.nc. According to the
the metadata, the file source was taken from
The Argo Global Data Assembly Center FTP
server at ftp://ftp.ifremer.fr/ifremer/argo.
Meanwhile the creator and the publisher is
NOAA therefore these data were acquired
from the US NOAA National Centers for
Environmental Information (NCEI) on July
28th 2018 from https://www.nodc.noaa.gov/.
There are three parameters that are
measured by the argo float which are
temperature, salinity and pressure.
Up to July 28th 2018 there are 215
stations observed that cover period of time
from July 2017 to July 2018 approximately
inside a box area limited by longitude 129.5°
E to 131.5° E and latitude 6° S to 4.5° S.
Fig. 5. Argo float BIDE distribution as of July 2018
Fig. 6 Argo float BIDE statistic as of July 2018
65
Fig. 7. Argo float BIDE time series 2017-2018
1.3. Methodology of Research
Several academic studies and field
researh had been performed before to
answer the question of how to present
tactical environment support from
Pushidrosal to Indonesian Navy. As early as
year 2000 the first study of oceanographic
layer chart concept for underwater warfare
operation and exercise support was
conducted by Commander Dede Yuliadi
M.Sc during his Indonesian Navy staf and
command school student period (Yuliadi,
2000). Since then several Indonesian Navy
officers started gaining interest on this topic
of research study, with some resorded topic
such as layer determination and its
underwater detection application (Jatmiko,
2001), underwater acoustic positioning
system for the submarine (Marwoto, 2007),
active sonar transmission loss (Ludfy, 2007),
Tactical Ocean Data Geographic
Informastion System for submarine (Taufiq,
2007), underwater acoustic propagation for
submarine navigation and communication
(Wahyudi, 2007), and tactical oceanography
map for submarine navigation (Asryanto,
2018).
One of closely related study about
sound speed propogation underwater
regarding it’s impact on navy operation was
conducted by Tri Aji et al who analyzed the
seasonal variability of thermocline, sound
speed and probable shadow zone in Sunda
strait published in 2017. This research was
utilizing dataset from INDESO project with
daily temporal resolution and ½ degrees
spatial resolution. Further explained in the
publication that the study applied 0.1 deg C
gradient for the thermocline layer
determinantion during four seasons data in
2014. As the results thermocline layer depth
variability and it’s sound speed range
variability which estimated as the probable
shadow zones for each four seasons and
annual average was presented (Aji, et al.,
2017). Another research about shadow zone
and sound wave underwater propagation
properties for navy sonar operation was
conducted by Mario Marco Wainarisi in 2016.
In his final study report he explained that this
research is utilizing in situ CTD observations
from The Transport, Internal Waves and
Mixing in The Indonesian Throughflow
Region (TIMIT) and Its Impact On Marine
Ecosystem 2015 Cruise of Indonesian
Marine and Fishery Ministry and CTD data
from NOAA World Ocean Atlas. The
research uses simulation pattern of
propagation of sound waves from KRI Cakra
– 401 propulsion system detected by FMS
21/3 sonar with passive sonar mode. The
simulation applied polynomial fitting and ray
tracing methodology under matlab
environment. The average pattern of sound
wave propagation simulation results shows
the presence of shadow zone and surface
duct on each lane detected in Makassar
Strait (Wainarisi, et al., 2017).
Compared to previously mentioned
studies this research paralelly equivalent with
Tri Aji’s research methodology which will
66
analyze the monthly average variations of
SLD parameter with further confirmation or
verification about the relation of SLD and
surface duct with polynomial fitting and ray
tracing methodology that applied by Marco’s
in his study. The use of CTD in situ
observation which cover a full annual cycle
make the study more realistic since generally
a full annual cycle analysis, especially in
Indonesia waters, can only be done from
model dataset because the scarcity of in situ
oceanography observations conducted until
now.
Technically the downloaded dataset
opened or imported into ODV software for
analysis preparation. Then sample selection
criteria is applied to filter the observation for
only the desired month to be processed. Two
derived variables of sound speed and depth
from pressure are added for station plotting
of temperature, salinity and sound speed. All
of measured profiles (temperature, salinity
and sound speed) are monthly averaged
using moving average methode that is
provided by ODV (Schlitzer, 2015). The
averaged value of sound speed for each
month then copied and saved in txt format.
This monthly average value of sound speed
then loaded into mathlab and investigated to
get the SLD using the alghoritm of searching
the depth of sound speed local maximum
(matlab, 2018) that is larger than any
shallower value and larger than the next
deeper value, which is usually coincide or
near to the MLD (Helber, et al., 2008). The
monthly average sound speed profile then
used to investigate the presence of surface
duct using the typical hull mounted sonar
source depth between 5 to 10 m applying the
previously mentioned polynomial fitting and
ray tracing methode. The confirmation will
not only indicating probable shadow zone but
more realistically simulate and produce a
graphical representation which reasonably
will give more assurance for the navy ships
on the sea.
Fig. 8 Sound speed profiles versus depth for (a)
standard, (b) multiple surface duct, (c) absent
surface duct, and (d) deep SLD cases
(Helber, et al., 2008)
Fig. 9 Flowchart of the research
67
2. RESULT AND DISCUSSION.
2.1. Result.
In this study the full annual cycle dataset is
imported into ODV then two derived variables which
are sound speed derived from temperature and
salinity and depth derived from pressure are added
to the list in order to use them for plotting and
further analysis. This very first process will result 12
sound speed profiles for each 12 months which
needed for the next step of SLD estimation.
Fig. 10 Monthly average sound velocity profiles
variation for the full annual cycle
The monthly average sound velocity profiles
result from ODV then saved into text file to be used
for SLD estimation using the searching alghoritm
for local maxima in matlab software. Local maxima
is a data sample that is either larger than its two
neighboring samples. Additional confirmation
plotting is applied for each sound velocity profiles to
ensure the exact position of SLD. Once the SLD
confirmed then polynomial fitting and ray tracing will
be applied with the input so-called sound velocity
profiles to produce the ray tracing plot which will
confirm the presence of surface duct or not.
68
Fig. 11 SLD estimated at about 16m depth, local
maxima sound speed 1543.30 m/s. Surface duct
simulated at about 0 - 30m depth
Fig. 12 SLD estimated at about 37m depth, local
maxima sound speed 1542.9 m/s. Surface duct
simulated at about 0 - 41m depth
Fig. 13 SLD estimated at about 59m depth, local
maxima sound speed 1542.86 m/s. Surface duct
simulated at about 0 - 44m depth
Fig. 14 SLD estimated at about 13m depth, local
maxima sound speed 1542.89 m/s. No surface duct
simulated
69
Fig. 15 SLD estimated at about 14m depth, local
maxima sound speed 1541.5 m/s. No surface duct
simulated
Fig. 16 SLD estimated at about 24m depth, local
maxima sound speed 1539.03 m/s. No surface duct
simulated
Fig. 17 SLD estimated at about 40m depth, local
maxima sound speed 1537.73 m/s. surface duct
simulated at about 0 – 96m depth
Fig. 18 SLD estimated at about 22m depth, local
maxima sound speed 1537.87 m/s. No surface
duct simulated
70
Fig. 19 SLD estimated at about 6m depth, local
maxima sound speed 1538.47 m/s. No surface
duct simulated
Fig. 20 SLD estimated at about 6m depth, local
maxima sound speed 1541.73 m/s. No surface
duct simulated
Fig. 21 SLD estimated at about 6m depth, local
maxima sound speed 1543.81 m/s. No surface
duct simulated
Fig. 22 SLD estimated at about 5m depth, local
maxima sound speed 1544.28 m/s. weak surface
duct simulated at about 0 – 60m depth
2.2. Discussion
The SLD estimation results monthly
variations in Banda Sea based on BIDE CTD
dataset. The deepest SLD estimation is in
march at 59m depth. The vertical distance of
SLD in March is paralel with the simulated
surface duct (0-44m). Actually March SLD
(59m) is deeper than July SLD (40m), but the
configured ray tracing simulation results
shows that July surface duct vertical distance
(96m) is thicker than March simulated surface
duct. Nevertheless, March ray tracing
71
produce bottom propagation ray tracing which
cover water column under the surface duct
while the July ray tracing leaves water
column under the surface duct empty without
acoustic propagation wave. It is revealed that
the depth of SLD estimate is positvely
correlated with the formation of surface duct
but acoustic wave propagation simulation is
very complex process that is not only
influenced by SLD but also other factor such
as the sound velocity gradient before and
after the local maxima. SLD estimation is
prerequisite process to ensure the indication
of the presence of surface duct, but to verify it
acoustic wave propagation simulation,
through ray tracing for example, should be
done to further confirm how good is an active
sonar performance in the current condition.
The formation of surface duct is good
indication that an active sonar detection
range will be extended in the channel
covered by surface duct. This is teoritically
happen due to in a surface duct propagation
the spreading loss is cylindrical spreading
loss which is equal to distance (r) meanwhile
in the other propagation such as bottom
bounce the spreading loss is spherical
spreading loss which is equal to square
distance (r2). Due to this reason under
surface duct propagation acoustic wave
energy will have less transmission loss which
means a better probability of detection
((FAS), 2013).
Table.1 SLD estimation and surface duct simulation
in Banda Sea
3. CONCLUSION.
Based on the results and discussion above,
the sound velocity profiles of Banda Sea varies
monthly hence the SLD for each month will also
vary depends on the temperature and salinity
profiles which are two key variables the sound
speed derived from. Single process of SLD
estimation throuh the searching alghoritm of local
maxima will not adequate to verify the presence of
surface duct as the most favourable acoustic wave
propagation for the navy ship operating active
sonar to detect submarine operating in upper layer
of the ocean. Polynomial fitting ray tracing process
of acoustic wave propagation further ensure the
relation between SLD estimates and the formation
of surface duct. Generally deeper SLD will result a
deeper surface duct but in the condition that there
are enough sound speed gradient before and after
local maxima. Therefore to more precisely predict
how is acoustic wave propagates whether or not it
forms surface duct, simulation should be performed
utilizing the alghoritm and theory that has been well
established by underwater acoustic scientists such
as polynomial fitting ray tracing that is applied in
this study. Actually sound speed underwater is
unique even diurnally due to the influence of sun
light. That is why “afternoon effect” term is very
popular in anti submarine warfare. During the
afternoon, the sea surface temperature reach it’s
maximum resulting a very strong negative sound
speed gradient which degrades the sonar detection
72
range heavily. The monthly variation analysis of
SLD will help underwater warfare operation analyst
officer in the navy to prepare and plan underwater
warfare operation without the need to perform own
in situ measurement which are again impractical
and relatively expensive for operational budget.
4. ACKNOWLEDGEMENT.
The authors would like to thank to Banda
Indonesia Through Flow Dynamic Experiment and
NOAA (USA) from which the data source retrieved
for this study, Institut Pertanian Bogor (IPB),
Indonesian Ministry of Research Technology and
Higher education (Ristekdikti), LEGOS (France),
IFREMER (France) by whom BIDE conducted and
supported. All work has been done in Marine and
Coastal Data Laboratory, Marine Research Centre
(Indonesian Ministry of Marine Affairs and
Fisheries).
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